0xB0Lesson 12 of 12

Where to go next

Review the core vocabulary, build a complete tiny classifier end to end, and pick your next track.

15 min 6-question quiz 1 code exercise

By the end of this lesson you can

Use the core AI vocabulary with confidence
Combine training, prediction and evaluation in one program
Choose a next step that fits your goals

You’ve gone from “what is AI?” to vectors, models, neural networks, vision, language and ethics. Here’s the vocabulary you’ve picked up - keep it handy.

Term	Meaning
Feature	A measurable input a model uses to make a prediction.
Label	The correct answer attached to a training example.
Vector	An ordered list of numbers representing one thing.
Dot product	Multiply matching entries of two vectors and add them up.
Regression	Predicting a number.
Classification	Predicting a category.
k-nearest neighbors	Predict by a majority vote of the k closest training examples.
Loss function	One number measuring how wrong the predictions are.
Overfitting	Memorizing the training data instead of learning a general pattern.
Gradient descent	Repeatedly nudging parameters downhill on the loss.
Learning rate	The size of each gradient descent step.
Epoch	One full pass through the training data.
Neural network	Layers of connected artificial neurons.
Activation function	The nonlinear function applied to a neuron’s weighted sum.
Bias (parameter)	The learned constant added to a weighted sum.
Backpropagation	Computing every weight’s gradient by working backward from the output.
Deep learning	Machine learning with many-layered neural networks.
Convolution	Sliding a small kernel over a grid, taking a weighted sum at each spot.
Token	A unit of text (often a subword) that a language model processes.
Embedding	A learned vector that places similar items near each other.
Hallucination	A fluent, confident, false statement from a generative model.
Reinforcement learning	Learning by trial and error from rewards.

Try it

Quick vocabulary sort

Which part of building a model does each term belong to?

0 of 8 sortedScore 0/0

“Features and labels”
“Activation function”
“Learning rate”
“Held-out test set”
“Embeddings”
“Epochs and batches”
“Per-group error rates”
“Train/test split”

Everything together

Here is a complete, tiny machine learning pipeline: split the data, train (compute each class’s average point, its centroid), predict (nearest centroid), and evaluate on held-out data. It’s a close cousin of both k-NN and k-means. The exercise asks you to write it yourself.

pipeline.py

1import math
2
3data = [((1.0, 1.2), "small"), ((1.4, 0.9), "small"), ((0.8, 1.1), "small"), ((1.2, 1.5), "small"),
4        ((4.1, 3.8), "large"), ((3.7, 4.2), "large"), ((4.4, 4.0), "large"), ((3.2, 2.6), "large")]
5train, test = data[:3] + data[4:7], [data[3], data[7]]
6
7centroids = {}
8for label in ("small", "large"):
9    points = [point for point, point_label in train if point_label == label]
10    centroids[label] = tuple(sum(values) / len(values) for values in zip(*points))
11
12def predict(point):
13    return min(centroids, key=lambda label: math.dist(point, centroids[label]))
14
15correct = sum(predict(point) == label for point, label in test)
16print(f"test accuracy: {correct}/{len(test)}")

Output

test accuracy: 2/2

Keep learning

Pick the track that matches what excited you most:

Machine Learning - logistic regression, decision trees, cross-validation, and a full capstone classifier with numpy.
Data Science - explore, clean and visualize real data with pandas.
Computer Vision - filters, segmentation, detection and CNNs in depth.
Natural Language Processing - from tokenizers to word vectors and transformers.
Large Language Models, Prompt Engineering and RAG - build with the models behind modern chatbots.

And some excellent free resources outside this site:

Elements of AI - a friendly, no-code introduction from the University of Helsinki.
Google’s Machine Learning Crash Course - the fundamentals in more depth, with interactive exercises.
Harvard CS50’s Introduction to AI with Python - a project-based university course.
3Blue1Brown’s neural networks series - beautiful visual intuition for networks and backpropagation.
fast.ai - a practical, code-first deep learning course.

Key takeaways

Every ML project follows the same shape: data → model → training → evaluation.
The core ideas - vectors, dot products, loss, gradient descent - reappear in every corner of AI.
Keep going with a deeper track, and build something small to make it stick.

Lesson quiz

6 questions · pass with 5 correct · up to 50 XP

Passing this quiz completes the lesson and keeps your streak going. Questions you miss come back in review sessions later.

Practice: write Python

Write Python in the editor and run it against sample inputs. Python runs locally in your browser using a WebAssembly runtime.

Exercise 1

Build a complete classifier

+25 XP

Each input line is x y label. The last 25% of lines (rounding down the number of training lines to int(count * 0.75)) are the test set; the rest are for training.

Train: for each label, compute the centroid (average x, average y) of its training points. Print them in alphabetical order as LABEL: (X, Y) with 2 decimal places.
Predict each test point’s label as the label of the nearest centroid, and print x y -> PREDICTED (actual ACTUAL) (format x and y with :g).
Print accuracy: P% (whole percent).

When it works, look at the mistake it makes on the fruit test: weight in grams (100-170) swamps the color score (2-8) in the distance - the scaling pitfall from the k-NN lesson.

Fruit

main.py

Loading editor…

Python runs in a sandboxed browser worker with a 60 second time limit. Its runtime loads from the Pyodide CDN; your code stays in this browser.

Questions about this lesson

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